Many regression tasks involve the restoration of data corrupted by some form of degradation. Examples include the denoising of photographs, renderings, or audio signals. Neural networks, and in particular “denoising autoencoders with skip connections” or U-Nets, have been demonstrated to yield superior performance for many such tasks. The neural network is trained to output a restored version of data when provided a corrupted version of the data as an input. Training is accomplished by running corrupted input data through the current network, comparing the output of the neural network (i.e., prediction) to the ground truth training target data that is a clean version of the data, and adjusting the neural network parameters by stochastic gradient descent with the aid of backpropagation.
Traditionally, training a neural network requires a large corpus of training data including different pairs (X′, Y) of noisy input samples X′ and clean target samples Y (i.e., ground truth training samples). In some scenarios, however, obtaining clean training samples is difficult or slow, while obtaining noisy examples is easy. An obvious example is a Monte Carlo rendering: a quick render with only a few samples per pixel is fast to compute, but a converged, noiseless image may take hours to produce. There is a need for addressing these issues and/or other issues associated with the prior art.